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AlSuwaidan L. Deep Learning Based Classification of Dermatological Disorders. Biomed Eng Comput Biol 2023; 14:11795972221138470. [PMID: 37533697 PMCID: PMC10392223 DOI: 10.1177/11795972221138470] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 07/01/2023] [Indexed: 08/04/2023] Open
Abstract
Automated medical diagnosis has become crucial and significantly supports medical doctors. Thus, there is a demand for inventing deep learning (DL) and convolutional networks for analyzing medical images. Dermatology, in particular, is one of the domains that was recently targeted by AI specialists to introduce new DL algorithms or enhance convolutional neural network (CNN) architectures. A significantly high proportion of studies in the field are concerned with skin cancer, whereas other dermatological disorders are still limited. In this work, we examined the performance of 6 CNN architectures named VGG16, EfficientNet, InceptionV3, MobileNet, NasNet, and ResNet50 for the top 3 dermatological disorders that frequently appear in the Middle East. An Image filtering and denoising were imposed in this work to enhance image quality and increase architecture performance. Experimental results revealed that MobileNet achieved the highest performance and accuracy among the CNN architectures and can classify disorder with high performance (95.7% accuracy). Future scope will focus more on proposing a new methodology for deep-based classification. In addition, we will expand the dataset for more images that consider new disorders and variations.
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Affiliation(s)
- Lulwah AlSuwaidan
- Lulwah AlSuwaidan, Innovation and Emerging Technologies Center, Digital Government Authority, PO Box 11112, Riyadh, Saudi Arabia.
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2
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Alonso-Belmonte C, Montero-Vilchez T, Arias-Santiago S, Buendía-Eisman A. [Translated article] Current State of Skin Cancer Prevention: A Systematic Review. ACTAS DERMO-SIFILIOGRAFICAS 2022. [DOI: 10.1016/j.ad.2022.04.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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3
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Situación actual de la prevención del cáncer de piel: una revisión sistemática. ACTAS DERMO-SIFILIOGRAFICAS 2022; 113:781-791. [DOI: 10.1016/j.ad.2022.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 04/03/2022] [Accepted: 04/12/2022] [Indexed: 11/20/2022] Open
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4
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Rogers T, McCrary MR, Yeung H, Krueger L, Chen SC. Dermoscopic Photographs Impact Confidence and Management of Remotely Triaged Skin Lesions. Dermatol Pract Concept 2022; 12:e2022129. [PMID: 36159122 PMCID: PMC9464534 DOI: 10.5826/dpc.1203a129] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/08/2021] [Indexed: 12/14/2022] Open
Abstract
Introduction Improving remote triage is crucial given expansions in tele-dermatology and with limited in-person care during COVID-19. In addition to clinical pictures, dermoscopic images may provide utility for triage. Objectives To determine if dermoscopic images enhance confidence, triage accuracy, and triage prioritization for tele-dermatology. Methods In this preliminary parallel convergent mixed-methods study, a cohort of dermatologists and residents assessed skin lesions using clinical and dermoscopic images. For each case, participants viewed a clinical image and determined diagnostic category, management, urgency, and decision-making confidence. They subsequently viewed the associated dermoscopy and answered the same questions. A moderated focus group discussion followed to explore perceptions on the role of dermoscopy in tele-dermatology. Results Dermoscopy improved recognition of malignancies by 23% and significantly reduced triage urgency measures for non-malignant lesions. Participants endorsed specific utilities of tele-dermoscopy, such as for evaluating pigmented lesions, with limitations including poor image quality. Conclusions Dermoscopic images may be useful when remotely triaging skin lesions. Standardized imaging protocols are needed.
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Affiliation(s)
- Tova Rogers
- Department of Dermatology, Emory University School of Medicine, Atlanta, Georgia, USA
| | | | - Howa Yeung
- Department of Dermatology, Emory University School of Medicine, Atlanta, Georgia, USA,Regional Telehealth Service, VISN 7, Duluth, Georgia, USA
| | - Loren Krueger
- Department of Dermatology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Suephy C Chen
- Regional Telehealth Service, VISN 7, Duluth, Georgia, USA,Department of Dermatology, Duke University, Durham, North Carolina, USA
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Milling M, Pokorny FB, Bartl-Pokorny KD, Schuller BW. Is Speech the New Blood? Recent Progress in AI-Based Disease Detection From Audio in a Nutshell. Front Digit Health 2022; 4:886615. [PMID: 35651538 PMCID: PMC9149088 DOI: 10.3389/fdgth.2022.886615] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/25/2022] [Indexed: 11/26/2022] Open
Abstract
In recent years, advancements in the field of artificial intelligence (AI) have impacted several areas of research and application. Besides more prominent examples like self-driving cars or media consumption algorithms, AI-based systems have further started to gain more and more popularity in the health care sector, however whilst being restrained by high requirements for accuracy, robustness, and explainability. Health-oriented AI research as a sub-field of digital health investigates a plethora of human-centered modalities. In this article, we address recent advances in the so far understudied but highly promising audio domain with a particular focus on speech data and present corresponding state-of-the-art technologies. Moreover, we give an excerpt of recent studies on the automatic audio-based detection of diseases ranging from acute and chronic respiratory diseases via psychiatric disorders to developmental disorders and neurodegenerative disorders. Our selection of presented literature shows that the recent success of deep learning methods in other fields of AI also more and more translates to the field of digital health, albeit expert-designed feature extractors and classical ML methodologies are still prominently used. Limiting factors, especially for speech-based disease detection systems, are related to the amount and diversity of available data, e. g., the number of patients and healthy controls as well as the underlying distribution of age, languages, and cultures. Finally, we contextualize and outline application scenarios of speech-based disease detection systems as supportive tools for health-care professionals under ethical consideration of privacy protection and faulty prediction.
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Affiliation(s)
- Manuel Milling
- EIHW–Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Florian B. Pokorny
- EIHW–Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- Research Unit iDN–interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz, Austria
- Division of Physiology, Otto Loewi Research Center, Medical University of Graz, Graz, Austria
| | - Katrin D. Bartl-Pokorny
- EIHW–Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- Research Unit iDN–interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz, Austria
- Division of Physiology, Otto Loewi Research Center, Medical University of Graz, Graz, Austria
| | - Björn W. Schuller
- EIHW–Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- GLAM–Group on Language, Audio, & Music, Imperial College London, London, United Kingdom
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6
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Foo LL, Ng WY, Lim GYS, Tan TE, Ang M, Ting DSW. Artificial intelligence in myopia: current and future trends. Curr Opin Ophthalmol 2021; 32:413-424. [PMID: 34310401 DOI: 10.1097/icu.0000000000000791] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PURPOSE OF REVIEW Myopia is one of the leading causes of visual impairment, with a projected increase in prevalence globally. One potential approach to address myopia and its complications is early detection and treatment. However, current healthcare systems may not be able to cope with the growing burden. Digital technological solutions such as artificial intelligence (AI) have emerged as a potential adjunct for myopia management. RECENT FINDINGS There are currently four significant domains of AI in myopia, including machine learning (ML), deep learning (DL), genetics and natural language processing (NLP). ML has been demonstrated to be a useful adjunctive for myopia prediction and biometry for cataract surgery in highly myopic individuals. DL techniques, particularly convoluted neural networks, have been applied to various image-related diagnostic and predictive solutions. Applications of AI in genomics and NLP appear to be at a nascent stage. SUMMARY Current AI research is mainly focused on disease classification and prediction in myopia. Through greater collaborative research, we envision AI will play an increasingly critical role in big data analysis by aggregating a greater variety of parameters including genomics and environmental factors. This may enable the development of generalizable adjunctive DL systems that could help realize predictive and individualized precision medicine for myopic patients.
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Affiliation(s)
- Li Lian Foo
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Wei Yan Ng
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | | | - Tien-En Tan
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Marcus Ang
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Daniel Shu Wei Ting
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
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Perpetuini D, Formenti D, Cardone D, Filippini C, Merla A. Regions of interest selection and thermal imaging data analysis in sports and exercise science: a narrative review. Physiol Meas 2021; 42. [PMID: 34186518 DOI: 10.1088/1361-6579/ac0fbd] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 06/29/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Infrared thermography (IRT) is a non-invasive, contactless and low-cost technology that allows recording of the radiating energy that is released from a body, providing an estimate of its superficial temperature. Thanks to the improvement of infrared thermal detectors, this technique is widely used in the biomedical field to monitor the skin temperature for different purposes (e.g. assessing circulatory diseases, psychophysiological state, affective computing). Particularly, in sports and exercise science, thermography is extensively used to assess sports performance, to investigate superficial vascular changes induced by physical exercise, and to monitor injuries. However, the methods of analysis employed to treat IRT data are not standardized, and hence introduce variability in the results. APPROACH This review focuses on the methods of analysis currently used for thermal imaging in sports and exercise science. MAIN RESULTS Firstly, the procedures employed for the selection of regions of interest (ROIs) from anatomical body districts are reviewed, paying attention also to the potentialities of morphing algorithms to increase the reproducibility of thermal results. Secondly, the statistical approaches utilized to characterize the temperature frequency and spatial distributions within ROIs are investigated, showing their strengths and weaknesses. Moreover, the importance of employing tracking methods to analyze the temporal thermal oscillations within ROIs is discussed. Thirdly, the capability of employing procedures of investigation based on machine learning frameworks on thermal imaging in sports science is examined. SIGNIFICANCE Finally, some proposals to improve the standardization and the reproducibility of IRT data analysis are provided, in order to facilitate the development of a common database of thermal images and to improve the effectiveness of IRT in sports science.
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Affiliation(s)
- David Perpetuini
- Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University G. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100, Chieti, Italy
| | - Damiano Formenti
- Department of Biotechnology and Life Sciences (DBSV), University of Insubria, Via Dunant, 3, 21100, Varese, Italy
| | - Daniela Cardone
- Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University G. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100, Chieti, Italy
| | - Chiara Filippini
- Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University G. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100, Chieti, Italy
| | - Arcangelo Merla
- Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University G. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100, Chieti, Italy
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8
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Comparison of machine learning strategies for infrared thermography of skin cancer. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102872] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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9
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Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review. SENSORS 2021; 21:s21010252. [PMID: 33401739 PMCID: PMC7795742 DOI: 10.3390/s21010252] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 12/24/2020] [Accepted: 12/26/2020] [Indexed: 02/04/2023]
Abstract
The worldwide incidence of skin cancer has risen rapidly in the last decades, becoming one in three cancers nowadays. Currently, a person has a 4% chance of developing melanoma, the most aggressive form of skin cancer, which causes the greatest number of deaths. In the context of increasing incidence and mortality, skin cancer bears a heavy health and economic burden. Nevertheless, the 5-year survival rate for people with skin cancer significantly improves if the disease is detected and treated early. Accordingly, large research efforts have been devoted to achieve early detection and better understanding of the disease, with the aim of reversing the progressive trend of rising incidence and mortality, especially regarding melanoma. This paper reviews a variety of the optical modalities that have been used in the last years in order to improve non-invasive diagnosis of skin cancer, including confocal microscopy, multispectral imaging, three-dimensional topography, optical coherence tomography, polarimetry, self-mixing interferometry, and machine learning algorithms. The basics of each of these technologies together with the most relevant achievements obtained are described, as well as some of the obstacles still to be resolved and milestones to be met.
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Haddadi N, Travis G, Nassif NT, Simpson AM, Marsh DJ. Toward Systems Pathology for PTEN Diagnostics. Cold Spring Harb Perspect Med 2020; 10:cshperspect.a037127. [PMID: 31615872 DOI: 10.1101/cshperspect.a037127] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Germline alterations of the tumor suppressor PTEN have been extensively characterized in patients with PTEN hamartoma tumor syndromes, encompassing subsets of Cowden syndrome, Bannayan-Riley-Ruvalcaba syndrome, Proteus and Proteus-like syndromes, as well as autism spectrum disorder. Studies have shown an increase in the risk of developing specific cancer types in the presence of a germline PTEN mutation. Furthermore, outside of the familial setting, somatic variants of PTEN occur in numerous malignancies. Here we introduce and discuss the prospect of moving toward a systems pathology approach for PTEN diagnostics, incorporating clinical and molecular pathology data with the goal of improving the clinical management of patients with a PTEN mutation. Detection of a germline PTEN mutation can inform cancer surveillance and in the case of somatic mutation, have value in predicting disease course. Given that PTEN functions in the PI3K/AKT/mTOR pathway, identification of a PTEN mutation may highlight new therapeutic opportunities and/or inform therapeutic choices.
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Affiliation(s)
- Nahal Haddadi
- School of Life Sciences, University of Technology Sydney, Ultimo, New South Wales 2007, Australia
| | - Glena Travis
- School of Life Sciences, University of Technology Sydney, Ultimo, New South Wales 2007, Australia
| | - Najah T Nassif
- School of Life Sciences, University of Technology Sydney, Ultimo, New South Wales 2007, Australia.,Centre for Health Technologies, University of Technology Sydney, Ultimo, New South Wales 2007, Australia
| | - Ann M Simpson
- School of Life Sciences, University of Technology Sydney, Ultimo, New South Wales 2007, Australia.,Centre for Health Technologies, University of Technology Sydney, Ultimo, New South Wales 2007, Australia
| | - Deborah J Marsh
- School of Life Sciences, University of Technology Sydney, Ultimo, New South Wales 2007, Australia.,Centre for Health Technologies, University of Technology Sydney, Ultimo, New South Wales 2007, Australia.,Translational Oncology Group, School of Life Sciences, Faculty of Science, University of Technology Sydney, Ultimo, New South Wales 2007, Australia.,Northern Clinical School, Kolling Institute, Faculty of Medicine and Health, University of Sydney, New South Wales 2006, Australia
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